Machine Learning
Kernel matching pursuit classifier ensemble
Pattern Recognition
Kernel matching pursuit for large datasets
Pattern Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Base vector selection for kernel matching pursuit
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Hi-index | 0.00 |
Kernel matching pursuit (KMP), as a greedy machine learning algorithm, appends iteratively functions from a kernel-based dictionary to its solution An obvious problem is that all kernel functions in dictionary will keep unchanged during the whole process of appending It is difficult, however, to determine the optimal dictionary of kernel functions ahead of training, without enough prior knowledge This paper proposes to further refine the results obtained by KMP, through adjusting all parameters simultaneously in the solutions Three optimization methods including gradient descent (GD), simulated annealing (SA), and particle swarm optimization (PSO), are used to perform the refining procedure Their performances are also analyzed and evaluated, according to experimental results based on UCI benchmark datasets.